A Task-oriented Chatbot Based on LSTM and Reinforcement Learning

Abstract

Thanks to the advancements in deep learning, chatbots are widely used in messaging applications. Undoubtedly, a chatbot is a new way of interaction between humans and machines. However, most of the chatbots act as a simple question answering system that responds with formulated answers. Traditional conversational chatbots usually adopt a retrieval-based model that requires a large amount of conversational data for retrieving various intents. Hence, training a chatbot model that uses low-resource conversational data to generate more diverse dialogues is desirable. We propose a method to build a task-oriented chatbot using a sentence generation model that generates sequences based on the generative adversarial network. The architecture of our model contains a generator that generates a diverse sentence and a discriminator that judges the sentences by comparing the generated and the ground-truth sentences. In the generator, we combine the attention model with the sequence-to-sequence model using hierarchical long short-term memory to extract sentence information. For the discriminator, our reward mechanism assigns low rewards for repeated sentences and high rewards for diverse sentences. Extensive experiments are presented to demonstrate the utility of our model that generates more diverse and information-rich sentences than those of the existing approaches.